SummaryWe envisage a new generation of dynamic holographic laser tweezers and stretching tools with unprecedented spatial control of gradient and scattering light forces, to unravel functional mysteries of cell biology and genetics: Based on our recently developed, highly successful and widely recognized amplitude and phase shaping techniques with cascaded spatial light modulators (SLM), we will create new holographic optical manipulators consisting of a line-shaped trap with balanced net scattering forces and controllable local phase-gradients. Combining these line stretchers with spiral phase contrast imaging or nonlinear optical microscopy will allow quantitative study of functional shape changes. The novel tool is hugely more versatile than standard optical tweezers, since direction and magnitude of the scattering force can be designed to precisely follow the structure. In combination with conventional multi-spot traps the line stretcher acts as a sensitive and adaptable local force sensor. In collaboration with local experts we want to tackle hot topics in Genetics, e.g. search for force profile signatures in regions with Copy Number Variations. Possibly the approach may shed light on basic physical characteristics such as, for example, chromosomal fragility in Fra(X) syndrome, the most common monogenic cause of mental retardation. The new design intrinsically offers enhanced microscopic resolution, as SLM-synthesized apertures and waveforms can enlarge the number of spatial frequencies forming the image. Ultimately, nonlinear holography can be implemented, sending phase shaped wavefronts to target samples. This can, e.g., be used to push the sensitivity of nonlinear chemical imaging, or for controlled photo-activation of targeted regions in neurons.

We envisage a new generation of dynamic holographic laser tweezers and stretching tools with unprecedented spatial control of gradient and scattering light forces, to unravel functional mysteries of cell biology and genetics: Based on our recently developed, highly successful and widely recognized amplitude and phase shaping techniques with cascaded spatial light modulators (SLM), we will create new holographic optical manipulators consisting of a line-shaped trap with balanced net scattering forces and controllable local phase-gradients. Combining these line stretchers with spiral phase contrast imaging or nonlinear optical microscopy will allow quantitative study of functional shape changes. The novel tool is hugely more versatile than standard optical tweezers, since direction and magnitude of the scattering force can be designed to precisely follow the structure. In combination with conventional multi-spot traps the line stretcher acts as a sensitive and adaptable local force sensor. In collaboration with local experts we want to tackle hot topics in Genetics, e.g. search for force profile signatures in regions with Copy Number Variations. Possibly the approach may shed light on basic physical characteristics such as, for example, chromosomal fragility in Fra(X) syndrome, the most common monogenic cause of mental retardation. The new design intrinsically offers enhanced microscopic resolution, as SLM-synthesized apertures and waveforms can enlarge the number of spatial frequencies forming the image. Ultimately, nonlinear holography can be implemented, sending phase shaped wavefronts to target samples. This can, e.g., be used to push the sensitivity of nonlinear chemical imaging, or for controlled photo-activation of targeted regions in neurons.

Max ERC Funding

1 987 428 €

Duration

Start date: 2010-05-01, End date: 2015-04-30

Project acronymCOMPLEX REASON

ProjectThe Parameterized Complexity of Reasoning Problems

Researcher (PI)Stefan Szeider

Host Institution (HI)TECHNISCHE UNIVERSITAET WIEN

Call DetailsStarting Grant (StG), PE6, ERC-2009-StG

SummaryReasoning, to derive conclusions from facts, is a fundamental task in Artificial Intelligence, arising in a wide range of applications from Robotics to Expert Systems. The aim of this project is to devise new efficient algorithms for real-world reasoning problems and to get new insights into the question of what makes a reasoning problem hard, and what makes it easy. As key to novel and groundbreaking results we propose to study reasoning problems within the framework of Parameterized Complexity, a new and rapidly emerging field of Algorithms and Complexity. Parameterized Complexity takes structural aspects of problem instances into account which are most significant for empirically observed problem-hardness. Most of the considered reasoning problems are intractable in general, but the real-world context of their origin provides structural information that can be made accessible to algorithms in form of parameters. This makes Parameterized Complexity an ideal setting for the analysis and efficient solution of these problems. A systematic study of the Parameterized Complexity of reasoning problems that covers theoretical and empirical aspects is so far outstanding. This proposal sets out to do exactly this and has therefore a great potential for groundbreaking new results. The proposed research aims at a significant impact on the research culture by setting the grounds for a closer cooperation between theorists and practitioners.

Reasoning, to derive conclusions from facts, is a fundamental task in Artificial Intelligence, arising in a wide range of applications from Robotics to Expert Systems. The aim of this project is to devise new efficient algorithms for real-world reasoning problems and to get new insights into the question of what makes a reasoning problem hard, and what makes it easy. As key to novel and groundbreaking results we propose to study reasoning problems within the framework of Parameterized Complexity, a new and rapidly emerging field of Algorithms and Complexity. Parameterized Complexity takes structural aspects of problem instances into account which are most significant for empirically observed problem-hardness. Most of the considered reasoning problems are intractable in general, but the real-world context of their origin provides structural information that can be made accessible to algorithms in form of parameters. This makes Parameterized Complexity an ideal setting for the analysis and efficient solution of these problems. A systematic study of the Parameterized Complexity of reasoning problems that covers theoretical and empirical aspects is so far outstanding. This proposal sets out to do exactly this and has therefore a great potential for groundbreaking new results. The proposed research aims at a significant impact on the research culture by setting the grounds for a closer cooperation between theorists and practitioners.